Many symbolic reasoning algorithms can be viewed as searching for a solution in a space defined by prior domain knowledge. Given sufficient domain knowledge represented in symbolic form, `difficult' reasoning problems, such as analogical reasoning, can be `solved' via exhaustive search, even though they are challenging for the average human. However, such algorithms operate on a symbolic space, whereas humans are easily able to consume rich data such as images or speech. Neural networks on the other end are proficient in encoding high-dimensional continuous data and are robust to noisy inputs, but struggle with deductive reasoning and absorbing explicit domain knowledge. `Neural Algorithmic Reasoning'[1]}, presents an approach to jointly model neural and symbolic learning, wherein rich inputs are encoded
to a latent representation that has been learned using from symbolic inputs.
This design allows neural learners and algorithms to complement each other's weaknesses. Through this work, we aim to investigate a variation of the neural algorithmic reasoning approach applied to analogical reasoning, using RAVENs Progressive Matrices [2]} problems as a test case.
<FIGURE>